SearcharxivSearch

arXiv · 2308.04500

Predicting Pathogenicity Of nsSNPs Associated With Rb1 -- An In Silico Approach

Abstract

Single nucleotide polymorphisms (SNPs) are variations at specific locations in DNA. Sequence responsible for marking genes associated with diseases or tracking inherited diseases within The family. These variations in the Rb1 gene can cause Retinoblastoma and cancer in the retina Of one eye or both, Osteosarcoma, Melanoma, Leukemias, Lungs, and Breast cancer. First of all,The SNP database hosted by NCBI was used to extract some principal data. The association of Rb1 to Other genes were analyzed by GeneMANIA. Ten different computational tools, i.eSIFT,Polyphen-2, I-Mutant 3.0,PROVEAN, SNAP2, PHD-SNP, PMut, SNPs&GO were used for the screening of damaging SNP for the estimation of conserved regions of amino acids Consurf Server was used for the evaluation of the structural stability of both native and mutant proteins, Project Hope was used to examine the structural effects of mutant protein.GeneMANIA predicted that RB1 Gene was expected to have a strong association with 20 other genes i.e. CCND1 and RBP2 etc. As per data retrieved from dbSNP hosted by NCBI,the Rb1 gene probed in this study carried a total of 36,358 SNPs. 345 were found in 3'UTR, 65 in 5'UTR, and 34,543 were found in the intron region. 844 were coding SNPs, and out of 844, 199 were synonymous And 450 were non-synonymous, including 425 missense, five nonsense, and 20 frameshift mutations. And remaining all are other types of SNPs. We took 425 missense SNPs for our investigation. A total of 17 mutations i.e. D332G, R445Q, E492V, P515T, W516G, V531G, E533K, E539K, M558R,W563G, L657Q, A658T, R661Q, D697H, D697E, P796L and R798W were predicted to have Damaging effects on structure and function of Rb1 protein..

Explore related subjects

Keep this discovery

BibTeXRIS

Anum Munir. 2023-07-16. Predicting Pathogenicity Of nsSNPs Associated With Rb1 -- An In Silico Approach. https://arxiv.org/abs/2308.04500

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models

We demonstrate the framework on three infectious diseases derived from a companion mechanistic immune-simulation platform: SARS-CoV-2, Influenza A Virus, and Plasmodium falciparum. Each disease was evaluated across hospitalization and intensive care unit cohorts, yielding six cohorts in total. Best-pipeline cross-validated macro F1 ranged from 0.82 for IAV-HOSP to 0.99 for COV-ICU, and the framework produced tiered, direction-aware biomarker lists for each disease and phase. Interleukin-18 (IL-18) reached the strongest tier in both SARS-CoV-2 phases with consistent direction. When benchmarked against three separate, independently collected clinical ICU datasets, MarkerScout's top-ranked features outperformed 94.4% of randomly selected feature sets of equivalent size for SARS-CoV-2, with a weaker but directionally consistent advantage for Influenza A Virus (66.7%) and Plasmodium falciparum (60.7%).

q-bio.OT

Enhancing Clinical Decision Support and Differential Diagnosis with Knowledge Graphs, and Retrieval Augmented Generation in Generative AI

Diagnostic error carries a burden, while unconstrained large language models (LLMs) remain vulnerable to hallucination and weak integration of quantitative laboratory dynamics. We developed a decision-support pipeline combining disease-specific biomarker correlation graphs, ordinary differential equations (ODEs), deep sequence classification, and retrieval-augmented generation (RAG). For 103 disease classes from a full blood count (FBC) repository, biomarker networks were used as coupling matrices to generate 30 trajectories per disease (3,090 total). A one-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) network classified disease trajectories and six dynamical clusters. A constrained GPT-4o-mini RAG layer used a 19-pattern BMJ Best Practice/NICE corpus to generate differential diagnoses evaluated for diagnostic suitability, evidential grounding, and clinical plausibility. Across five random-seed runs, disease-level accuracy was $0.940 \pm 0.006$ for the CNN (95\% CI 0.933--0.948) and $0.852 \pm 0.019$ for the LSTM (95\% CI 0.828--0.875); the CNN advantage was 8.87 percentage points (95\% CI 6.47--11.27; $t(4)=10.26$, $p=5.1\times10^{-4}$; Hedges' $g=3.67$). Among 100 sampled RAG cases, 96 parsed successfully; evidence was cited in 97.9\%, the true diagnosis was mentioned in 71.9\%, and the composite score was 3.82/5 with a 47.9\% strict pass rate. The central finding was a decoupling between grounding and diagnostic correctness: classifier-correct versus classifier-wrong outputs differed in diagnostic suitability but not evidential grounding. Post-hoc analysis confirmed a 1.02-point diagnostic-score difference (Mann--Whitney $p=0.0024$; Hedges' $g=0.72$), whereas grounding differed by only $-0.02$ points ($p=0.839$; $g=-0.04$).

q-bio.OT

Expanding the Human Ancestry Ontology to include under-represented populations and ethnicities for broader utility in annotations

Successful discovery, integration and reuse of data relies on the availability of rich, well-structured and machine-readable metadata to describe every aspect of the data, from sample sources to collection processes to experimental protocols. The use of standardised terminologies to express concepts in a harmonised fashion lies at the core of high-quality data annotation, increasing the FAIRness of the data, facilitating data integration and promoting reproducibility. Here, we describe the Human Ancestry Ontology (HANCESTRO), originally developed to improve standardised reporting of genetic ancestry genomic resources such as the NHGRI-EBI GWAS Catalog and the Human Cell Atlas through high-level population descriptors, and more recently expanded to include diverse and previously under-represented populations in genomics and genetics research. HANCESTRO provides a framework for population descriptors that includes both ancestry based on the analysis of genetic information and self-reported ethnicity, which is based on social and cultural factors that don't necessarily align with genetic populations. By enabling the accurate and interoperable representation of population-related data, it promotes inclusive, representative and reproducible science.

q-bio.OT